docstr: Computes events component of the model
  
  Parameters
  ----------
      features : torch.Tensor, float
          Features (either additive or multiplicative) related to event component dims (batch, n_forecasts,
          n_features)
      params : nn.Parameter
          Params (either additive or multiplicative) related to events
      indices : list of int
          Indices in the feature tensors related to a particular event
  Returns
  -------
      torch.Tensor
          Forecast component of dims (batch, n_forecasts)
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code: def scalar_features_effects(self, features, params, indices=None):
      """
      Computes events component of the model
  
      Parameters
      ----------
          features : torch.Tensor, float
              Features (either additive or multiplicative) related to event component dims (batch, n_forecasts,
              n_features)
          params : nn.Parameter
              Params (either additive or multiplicative) related to events
          indices : list of int
              Indices in the feature tensors related to a particular event
      Returns
      -------
          torch.Tensor
              Forecast component of dims (batch, n_forecasts)
      """
      if indices is not None:
          features = features[:, :, indices]
          params = params[:, indices]
  
      return torch.sum(
          features.unsqueeze(dim=2)
          * params.unsqueeze(dim=0).unsqueeze(dim=0),
          dim=-1,
      )
  
file_path: neural_prophet/neuralprophet/components/future_regressors/linear.py
imports: import torch
  import torch.nn
  from neuralprophet.components.future_regressors import FutureRegressors
  from neuralprophet.utils_torch import init_parameter
label_desc: Use image node_id 2 for calling the LinearFutureRegressors obj's underlying member method code with example usage: obj.scalar_features_effects(features, params, indices) and returns: torch